Don't miss our weekly PhD newsletter | Sign up now Don't miss our weekly PhD newsletter | Sign up now

  Fast modeling and computing for heterogeneously high-throughput data association


   Department of Mathematical Sciences

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Dr S Zhu  Applications accepted all year round  Funded PhD Project (Students Worldwide)

About the Project

The sheer size and scope, as well as heterogeneity in big data sets pose many challenges in all aspects, and bring great fundamental problems and promising interdisciplinary research opportunities. Learning and mining latent association from high-throughput data and making a reasonable statistical inference or recommendation is one of the central research themes. With the soaring of accumulated high-throughput data from breeding, clinical trials, precision medicine, searching engines, social networks and recommendation systems, software packages that fit models to such types must be efficient enough to support big data analytics. Therefore, developing robust statistical methods, designing scalable and efficient algorithms for efficient inference or recommendation becomes one of the increasing important research theme as the data sets increase. The PhD candidates is supposed to work on relevant fundamental problems, to improve our ability to analyze, predict, and optimize these fast-moving and massive-scale data sets.

For more information about doctoral scholarship and PhD programme at Xi’an Jiaotong-Liverpool University (XJTLU): Please visit
http://www.xjtlu.edu.cn/en/study-with-us/admissions/entry-requirements
http://www.xjtlu.edu.cn/en/admissions/phd/feesscholarships.html

Requirements
The candidate should have a first class or upper second class honours degree, or a master’s degree (or equivalent qualification), in mathematics, statistics, computer science, bioinformatics or other relevant subjects. Evidence of good spoken and written English is essential. The candidate should have an IELTS score of 6.5 or above, if the first language is not English. This position is open to all qualified candidates irrespective of nationality.

Degree
The student will be awarded a PhD degree from the University of Liverpool (UK) upon successful completion of the program.

How to Apply
Interested applicants are advised to email [Email Address Removed] (XJTLU principal supervisor’s email address) the following documents for initial review and assessment (please put the project title in the subject line).

• CV
• Two reference letters with company/university letterhead
• Personal statement outlining your interest in the position
• Proof of English language proficiency (an IELTS score of 6.5 or above)
• Verified school transcripts in both Chinese and English (for international students, only the English version is required)
• Verified certificates of education qualifications in both Chinese and English (for international students, only the English version is required)

Informal enquiries may be addressed to Dr Shengxin Zhu ([Email Address Removed]), whose personal profile is linked below,
http://www.xjtlu.edu.cn/en/departments/academic-departments/mathematicalsciences/staff/shengxin-zhu

Funding Notes

The PhD studentship is available for three years subject to satisfactory progress by the student. The award covers tuition fees for three years (currently equivalent to RMB 80,000 per annum) and provides a monthly stipend of 5000 RMB as a contribution to living expenses. It also provides up to RMB 16,500 to allow participation at international conferences during the period of the award. It is a condition of the award that holders of XJTLU PhD scholarships carry out 300-500 hours of teaching assistance work per year. The scholarship holder is expected to carry out the major part of his or her research at XJTLU in Suzhou, China. However, he or she is eligible for a research study visit to the University of Liverpool of up to three months, if this is required by the project.